In image quality evaluation and/or camera evaluation contexts, such evaluations may attempt to provide a quantitative or qualitative quality assessment of the images or camera used to attain such images. Current techniques for objective quality assessment of images or cameras may have numerous drawbacks and limitations. For example, techniques that use reference images may require a laboratory environment with controlled lighting and an imaging expert to perform controlled test image comparisons against reference charts of various types. Such expensive, time consuming, and onerous benchmarking methods have prevented consistent adoption by the industry, technical press, and consumers. Furthermore, such techniques may estimate individual quality aspects, but may not assess overall human perceptual quality. For example, such techniques may be trained on image databases with single, linear distortions that cannot reliably assess consumer images that contain multiple, subtle and complex distortions.
Current no-reference image quality assessment techniques may perform relatively well with linear simulated distortions, but do not provide the sophistication or accuracy necessary to provide reliable quality evaluation results for real world photos. For example, such techniques may fail to predict image quality accurately and in agreement with human subjective judgments on consumer-type photos captured by consumer devices such as phones and tablets.
It may be advantageous to perform no-reference image and video quality evaluations that are accurate and in agreement with human subjective judgments. It is with respect to these and other considerations that the present improvements have been needed. Such improvements may become critical as the desire to evaluate images, video, and cameras becomes more widespread.